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Covers the basics of machine learning for engineers, including grading, course requirements, practical examples, AI concepts, and ML applications.
Explores Deep Knowledge Tracing and its application in predicting student learning outcomes using neural networks and loss functions.
Explores the ethical implications of AI, emphasizing transparency and accountability in its applications.
Explores Recurrent Neural Networks for behavioral data, covering Deep Knowledge Tracing, LSTM, GRU networks, hyperparameter tuning, and time series prediction tasks.
